Сравнение на методи

	Байесов модел на векторна авторегресия (BVAR)	Структурна векторна авторегресия (SVAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1984	1980
Създател	Doan, Litterman & Sims	Sims (1980); identification schemes by Blanchard & Quah (1989)
Тип	Multivariate time-series model	Multivariate time series model
Основополагащ източник	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.	Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655-673.
Други названия	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	SVAR, structural vector autoregression, identified VAR, structural VAR model
Свързани	5	5
Резюме	The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.	Structural VAR extends the reduced-form VAR by imposing economic theory-based restrictions that identify orthogonal structural shocks. This allows researchers to disentangle the causal effects of distinct economic disturbances — such as supply versus demand shocks — and trace their dynamic propagation through a system of variables via impulse response functions and forecast error variance decompositions.